Misalignment angle will result in a considerable error for the integration of Doppler velocity log (DVL) and of Strapdown Inertial Navigation System (SINS). In this article, a robust initial alignment method for SINS/DVL is proposed to solve a practical applicable issue, which is that the outputs of DVL are often corrupted by the outliers. First, the alignment principle for SINS/DVL is summarized. Second, based on the principle of this alignment method, the apparent velocity model is investigated, and the parameters expression of the apparent velocity model are derived detailed. Using the apparent velocity model, the unknown parameters of the apparent velocity model are estimated by the developed robust Kalman filter, then the reconstructed observation vector, where the outliers are detected and isolated, is reconstructed by the estimated parameters. Based on the reconstructed observation vectors, the initial attitude is determined. Finally, the simulation and field tests are carried out to verify the performance of the proposed method. The test results are shown that the proposed method can detect and isolate the outliers effectively and get better performance than the previous work.

中文翻译：

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基于重构观测向量的SINS / DVL鲁棒初始对准

失准角将对多普勒速度测井（DVL）和捷联惯性导航系统（SINS）的集成造成相当大的误差。本文提出了一种鲁棒的SINS / DVL初始对准方法，以解决实际应用中的问题，即DVL的输出经常被异常值破坏。首先，总结了SINS / DVL的对齐原理。其次，基于这种对准方法的原理，研究了视在速度模型，并详细推导了视在速度模型的参数表达式。使用视在速度模型，先通过开发的鲁棒卡尔曼滤波器估计视在速度模型的未知参数，然后再重建观察到的向量，在该向量中检测并隔离异常值，通过估计的参数重建。基于重构的观察向量，确定初始姿态。最后，通过仿真和现场测试验证了所提方法的性能。测试结果表明，所提出的方法可以有效地检测和隔离离群值，并且比以前的工作具有更好的性能。